The explosive rise in popularity of smart phones has exposed the capacity limitation of the current cellular networks. The increasing usage of bandwidth-demanding multimedia and social networking applications on mobile devices further exacerbates the problem. To cope with the exponential growth in wireless data traffic, it is anticipated that significantly denser deployment of access nodes will be required in the future. Such dense deployment may be achieved by augmenting the existing base stations with a denser mix of “smaller” (pico or femto) base stations with reduced signal footprints.
As the distance among base stations reduces, the impact of their mutual interference can become more significant than that in the current cellular network due to the reduced path loss, the increased probability of direct line-of-sight interference, and the overlapping coverage of base stations from different hierarchical layers. This can substantially limit the achievable data throughput of individual users that can otherwise be attainable with the dense deployment. This is especially true for those users located near the cell boundary. Intelligent methods of coordinating transmission among neighboring base stations to reduce their mutual interference in a dense network are therefore highly desirable. Since future traffic pattern is not known a priori and may evolve differently in different geographical areas, it is also desirable for these methods of interference management to be scalable with, and autonomously adaptable to, any new deployment patterns.
In traditional cellular networks, little coordination is performed among the transmissions of neighboring base stations. Each base station typically tries to maximize the throughput of their own users in a greedy manner and relies on the receivers to suppress interference through the use of advanced receiver algorithms. Interference management at the network side is mainly accomplished through careful planning of frequency reuse patterns across the network to avoid using the same set of radio resource simultaneously in adjacent cells. However, as the demand for wireless data services continue to increase, the operators would have to aggressively reuse radio resources in adjacent cells and utilize more advanced interference coordination methods to mitigate interference at the network side.
One proposal is that interference coordination and radio resource management over an entire network in a metropolitan area can be performed in a central processing unit. Although such a centralized solution provides flexibility in managing and sharing network equipments, it also has its share of potential drawbacks. First, it demands fast backhaul communication links between the base stations and the centralized unit in order to make available the signal received from each base station at the central unit in a timely manner for coordination. For the network to be able to dynamically handle interference coordination for varying load patterns in a short time frame, significant latency-intolerant traffic need to be communicated over the backhaul. The cost of installing the required high-capacity backhaul links can be prohibitive for many developed countries. Moreover, the reliability of the network over a wide area depends solely on the central unit, implying a relatively low fault-tolerance as any malfunctions and system downtimes can be reflected on the entire network. Hence, it can be more vulnerable to malicious attacks. Furthermore, the central unit and its associated backhaul may not scale easily with new deployment of base stations in response to the future traffic growth, since adding a new cell site requires not only a new backhaul connection to the central unit but also a possible change in the processing algorithms to accommodate the new site in the overall coordination task.
Due to the potential drawbacks of centralized solutions, methods of distributing the task of interference coordination over all base stations in the network have received considerable interest recently. Decentralized interference coordination is robust to equipment failure since any equipment malfunctions only affect the local network performance and may readily be compensated by neighboring base stations in a dense network. It also tends to scale better with new deployment of base stations in response to traffic growth as no backhaul connection between new base stations and a remote central unit is needed. Furthermore, it facilitates parallel processing and can reap more benefits from the economy of scale than the centralized solutions.
For a base station equipped with more than one transmit antennas, one important aspect of interference coordination is to select beamforming weights for its antennas so that it can focus the radio signal energy on the user equipment (or mobile) being served while limiting the impact of its interference to those users served by other base stations. Most of the existing distributed methods of computing beamforming weights require each base station to be able to communicate with all other base stations in the network. Such distributed solutions still impose challenging latency requirements on the backhaul in a large network. Moreover, in many future wireless data communication systems, such as the Long-Term Evolution (LTE) and WiMax, predefined codebooks of beamforming weights were standardized so that equipment manufacturers can exploit the structure of the codebook to maximize the amplifier efficiency and to reduce the complexity of computing precoded signals. Most of the existing distributed methods of computing beamforming weights do not work when they are restricted to come from a predefined, standard codebook.